Bayesian Forecasting of WWW Traffic on the Time Varying Poisson Model
Abstract
Traffic forecasting from past observed traffic data with small calculation complexity is one of important problems for planning of servers and networks. Focusing on World Wide Web (WWW) traffic as fundamental investigation, this paper would deal with Bayesian forecasting of network traffic on the time varying Poisson model from a viewpoint from statistical decision theory. Under this model, we would show that the estimated forecasting value is obtained by simple arithmetic calculation and expresses real WWW traffic well from both theoretical and empirical points of view.
Cite
@article{arxiv.0906.3923,
title = {Bayesian Forecasting of WWW Traffic on the Time Varying Poisson Model},
author = {Daiki Koizumi and Toshiyasu Matsushima and Shigeichi Hirasawa},
journal= {arXiv preprint arXiv:0906.3923},
year = {2009}
}
Comments
8 pages, 6 figures. This paper was published in Proceeding of The 2009 International Conference on Parallel and Distributed Processing Techniques and Applications (PDPTA'09) in July, 2009. In version of v4, research grants are included in acknowledgment